| Feb. 13, 2024, 4:20 p.m. |
Created
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[{"model": "core.projectfund", "pk": 64547, "fields": {"project": 12767, "organisation": 2, "amount": 415416, "start_date": "2020-07-01", "end_date": "2023-12-31", "raw_data": 182371}}]
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| Jan. 30, 2024, 4:25 p.m. |
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[{"model": "core.projectfund", "pk": 57379, "fields": {"project": 12767, "organisation": 2, "amount": 415416, "start_date": "2020-07-01", "end_date": "2023-12-31", "raw_data": 160903}}]
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| Jan. 2, 2024, 4:15 p.m. |
Created
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[{"model": "core.projectfund", "pk": 50212, "fields": {"project": 12767, "organisation": 2, "amount": 415416, "start_date": "2020-07-01", "end_date": "2023-12-31", "raw_data": 137179}}]
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| Dec. 5, 2023, 4:24 p.m. |
Created
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[{"model": "core.projectfund", "pk": 42963, "fields": {"project": 12767, "organisation": 2, "amount": 415416, "start_date": "2020-06-30", "end_date": "2023-12-31", "raw_data": 108616}}]
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| Nov. 27, 2023, 2:15 p.m. |
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{"external_links": []}
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| Nov. 21, 2023, 4:40 p.m. |
Created
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[{"model": "core.projectfund", "pk": 35673, "fields": {"project": 12767, "organisation": 2, "amount": 415416, "start_date": "2020-06-30", "end_date": "2023-12-31", "raw_data": 66756}}]
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| Nov. 21, 2023, 4:40 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 105062, "fields": {"project": 12767, "organisation": 15994, "role": "PP_ORG"}}]
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| Nov. 21, 2023, 4:40 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 105061, "fields": {"project": 12767, "organisation": 15991, "role": "PP_ORG"}}]
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| Nov. 21, 2023, 4:40 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 105060, "fields": {"project": 12767, "organisation": 15834, "role": "PP_ORG"}}]
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| Nov. 21, 2023, 4:40 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 105059, "fields": {"project": 12767, "organisation": 12798, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:40 p.m. |
Created
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[{"model": "core.projectperson", "pk": 66071, "fields": {"project": 12767, "person": 17903, "role": "RESEARCH_COI_PER"}}]
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| Nov. 21, 2023, 4:40 p.m. |
Created
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[{"model": "core.projectperson", "pk": 66070, "fields": {"project": 12767, "person": 17904, "role": "PI_PER"}}]
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| Nov. 20, 2023, 2:05 p.m. |
Updated
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{"title": ["", "Probabilistic Tomography of Wireless Networks"], "description": ["", "\nLarge-scale wireless networks are expected to become prevalent in various Internet-of-Things (IoT) applications involving environment sensing and monitoring, communications, and computing. It is a fundamental task of many networks to deduce the network topology, both during the establishment of the network and periodically as the network state evolves. The availability of network topology and performance information is crucial for the operation and management of large wireless systems comprising low-power devices that are required to provide low-latency, high-reliability services. For example, state-of-the-art smart meter networks require this information to carry out routing and resource scheduling tasks, and the estimation of the number of devices in a network is useful for finding out how many sensors are still active or for detecting failures of some subnetworks. Inferring topology information even possess great importance in matters of national security in which one may have to learn the structure of a target network passively from external observables, such as the spectral activity of devices, without having access to the network devices and protocols. \n\nMany network characteristics can be inferred by observing end-to-end data, which often takes the form of packet probes. The general field of study concentrating on such techniques is known as "network tomography". Over the past twenty years, this field has been developed to include the inference of link loss statistics (loss tomography), internal queuing delays (delay tomography), and structural characteristics (topology tomography). Much of the work to date has focused on the formulation of optimal and efficient estimation methods that are primarily geared toward computer networks that exhibit certain constraints on their topologies. \n\nSome more recent studies of network tomography have considered wireless systems. However, investigations have largely been limited by the lack of available statistical models that incorporate spatial and physical characteristics inherent to wireless networks. For example, spatial (wireless) networks exhibit distinctive features (e.g., transitivity, clustering), which have not been fully exploited in topology inference tasks. \n\nThis project is concerned with developing improved active methods (topology discovery) and passive techniques (topology inference) of obtaining the topology of a wireless communications network or a portion thereof. The underlying hypothesis is that probabilistic knowledge of structural properties of wireless networks can be used as prior information to improve network inference tasks, particularly topology tomography, in practical systems. The project will begin with fundamental research into the correct modelling and statistical characterisation of wireless networks designed for particular applications, such as smart meter infrastructure and tactical systems. The results of this research will be exploited to develop new topology tomography algorithms that are optimised for use in the chosen applications. The technical contributions of the project will be accompanied and supported by a number of activities aimed at delivering impact through dissemination and technology transfer. The project is supported by three hands-on partners (Toshiba, Moogsoft, and HMGCC), each of which is at the leading edge of its respective field.\n\n"], "extra_text": ["", "\n\nPotential Impact:\nThis proposal concerns wireless communication networks, which underpin many sectors and service-oriented businesses in the UK and worldwide. It addresses a fundamental task of large-scale wireless networks in Internet-of-Things applications, which is to deduce the network topology, both during the establishment of the network and periodically as the network state evolves. The project will provide innovative results on the tomography of spatial networks (wireless networks in particular), in terms of both theoretical limits and insights, and practical solutions for topology discovery and inference. \n\nThe proposal benefits from the strong support of industrial (Toshiba, Moogsoft) and government (HMGCC) partners. All partners contributed to the development of this proposal and have clearly laid out their intentions for being involved. Toshiba is at the forefront of mesh network R&D, with emphasis on the Industrial IoT sector. At present, the Bristol Research & Innovation Lab is heavily involved in constructing scalable methods for 6TiSCH network operation, and the lab will deploy a 200-node network in South Gloucestershire (to be completed in 2020) as part of these efforts. Researchers in the lab have identified topology discovery as being a crucial task that will limit network scaling in practice. Toshiba will assist in translating project results into practical solutions through implementation and testing in their simulation platform and in the test network. The lab will adapt this work for use in up to eight million smart meter devices in the next few years. On the other hand, HMGCC will assist with the development of topology inference technology to support matters of national security. Finally, Moogsoft has fundamental interest in the theoretical tools that the project will explore and develop; from a practical perspective, the company hopes to utilise topology tomography for fault localisation. This diverse involvement will yield the following impact-related outcomes: (1) the likelihood of transferring new results to industry for the development of products and services will be maximised; (2) early-career researchers will be trained across a broad-base curriculum that includes exposure to both theoretical concepts and practical considerations; (3) the international IEEE ComSoc and SigProc communities will recognise the project and its partners as forming a virtual centre of excellence in spatial network tomography and graph signal processing, which will draw skilled researchers to the UK and will attract inward investment from international organisations. \n\nFurthermore, Prof. Coon has strong links to several other industrial organisations in the UK (e.g., BT, Orange Group, EE, Telefonica, and various start-ups, such as Animal Dynamics in Oxford), and through these, he will engage with non-partner organisations to disseminate results more broadly in the commercial sector. Cellular operators will benefit from this knowledge in that it will inform their interest IoT activities, whereas the project results could be used by Animal Dynamics in developing leading technology in unmanned aerial vehicle mesh networks.\n\n\n"], "status": ["", "Active"]}
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| Nov. 20, 2023, 2:05 p.m. |
Added
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{"external_links": [51032]}
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| Nov. 20, 2023, 2:05 p.m. |
Created
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[{"model": "core.project", "pk": 12767, "fields": {"owner": null, "is_locked": false, "coped_id": "5d393891-8b93-4845-9fdc-1c8d2299b5d3", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 66739, "created": "2023-11-20T13:47:42.123Z", "modified": "2023-11-20T13:47:42.123Z", "external_links": []}}]
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